Reconciliation of human and machine speech recognition performance

Misha Pavel, Malcolm Slaney, Hynek Hermansky

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper focuses on resolving a number of issues that appear when the performance of human speech recognition is compared to that of automatic speech recognition. In particular human experimental data suggest that the resulting error is a product of the individual streams. On the other hand, Bayesian combination requires a multiplication of the estimates of prior probabilities and likelihoods. We show that, in principle, there is no discrepancy. The product of errors is a performance measure and human and machine performance may be consistent with this empirically established regularity. The product of probabilities is step in an algorithm to achieve the performance that may or may not be consistent with the product of errors. The main problem is that most of prior discussions failed to distinguish the performance measures from the estimates of the parameters used in the algorithm.

Original languageEnglish (US)
Title of host publication2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
Pages1669-1672
Number of pages4
DOIs
StatePublished - 2009
Externally publishedYes
Event2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 - Taipei, Taiwan, Province of China
Duration: Apr 19 2009Apr 24 2009

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
Country/TerritoryTaiwan, Province of China
CityTaipei
Period4/19/094/24/09

Keywords

  • Pattern recogntion
  • Speech recogntion

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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